OpenRaiser/Intern-Atlas huggingface dataset:
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment
Venue: arXiv cs.LG 2019
Year: 2019

Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learning model is no difference - it is an engineering process with a life cycle, including design, implementation, tuning, testing, and deployment. However, most, if not all, existing continuous integration engines do not support machine learning as first-class citizens. In this paper, we present ease.ml/ci, to our best knowledge, the first continuous integration system for machine learning. The challenge of building ease.ml/ci is to provide rigorous guarantees, e.g., single accuracy point error tolerance with 0.999 reliability, with a practical amount of labeling effort, e.g., 2K labels per test. We design a domain specific language that allows users to specify integration conditions with reliability constraints, and develop simple novel optimizations that can lower the number of labels required by up to two orders of magnitude for test conditions popularly used in real production systems.
Provably scale-covariant networks from oriented quasi quadrature measures in cascade
Venue: arXiv cs.CV 2019
Year: 2019

This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computations. Based on a functional model of complex cells in terms of an oriented quasi quadrature combination of first- and second-order directional Gaussian derivatives, we couple such primitive computations in cascade over combinatorial expansions over image orientations. Scale-space properties of the computational primitives are analysed and it is shown that the resulting representation allows for provable scale and rotation covariance. A prototype application to texture analysis is developed and it is demonstrated that a simplified mean-reduced representation of the resulting QuasiQuadNet leads to promising experimental results on three texture datasets.
Hedonic Diversity Games
Venue: arXiv cs.GT 2019
Year: 2019

We consider a coalition formation setting where each agent belongs to one of the two types, and agents' preferences over coalitions are determined by the fraction of the agents of their own type in each coalition. This setting differs from the well-studied Schelling's model in that some agents may prefer homogeneous coalitions, while others may prefer to be members of a diverse group, or a group that mostly consists of agents of the other type. We model this setting as a hedonic game and investigate the existence of stable outcomes using hedonic games solution concepts. We show that a core stable outcome may fail to exist and checking the existence of core stable outcomes is computationally hard. On the other hand, we propose an efficient algorithm to find an individually stable outcome under the natural assumption that agents' preferences over fractions of the agents of their own type are single-peaked.
TamperNN: Efficient Tampering Detection of Deployed Neural Nets
Venue: arXiv cs.CR 2019
Year: 2019

Neural networks are powering the deployment of embedded devices and Internet of Things. Applications range from personal assistants to critical ones such as self-driving cars. It has been shown recently that models obtained from neural nets can be trojaned ; an attacker can then trigger an arbitrary model behavior facing crafted inputs. This has a critical impact on the security and reliability of those deployed devices. We introduce novel algorithms to detect the tampering with deployed models, classifiers in particular. In the remote interaction setup we consider, the proposed strategy is to identify markers of the model input space that are likely to change class if the model is attacked, allowing a user to detect a possible tampering. This setup makes our proposal compatible with a wide range of scenarios, such as embedded models, or models exposed through prediction APIs. We experiment those tampering detection algorithms on the canonical MNIST dataset, over three different types of neural nets, and facing five different attacks (trojaning, quantization, fine-tuning, compression and watermarking). We then validate over five large models (VGG16, VGG19, ResNet, MobileNet, DenseNet) with a state of the art dataset (VGGFace2), and report results demonstrating the possibility of an efficient detection of model tampering.
Interpretation of NDTM in the definition of NP
Venue: arXiv cs.CC 2019
Year: 2019

In this paper, we interpret NDTM (NonDeterministic Turing Machine) used to define NP by tracing to the source of NP. Originally NP was defined as the class of problems solvable in polynomial time by a NDTM in the theorem of Cook, where the NDTM was represented as Query Machine of essence Oracle. Later a model consisting of a guessing module and a checking module was proposed to replace the NDTM. This model of essence TM has a fundamental difference from the NDTM of essence Oracle, but people still use the term NDTM to designate this model, which leads to the disguised displacement of NDTM and produces out the verifier-based definition of NP as the class of problems verifiable in polynomial time by a TM (Turing Machine). This verifier-based one has been then accepted as the standard definition of NP where comes from the famous equivalence of the two definitions of NP. Since then the notion of nondeterminism is lost from NP, which causes ambiguities in understanding NP and then great difficulties in solving the P versus NP problem. Since NP is originally related with Oracle that comes from the work of Turing work about Computability, it seems quite necessary to trace back to the work of Turing and clarify further the issue about NP.
Learning To Follow Directions in Street View
Venue: arXiv cs.AI 2019
Year: 2019

Navigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision. We propose an instruction-following task that requires all of the above, and which combines the practicality of simulated environments with the challenges of ambiguous, noisy real world data. StreetNav is built on top of Google Street View and provides visually accurate environments representing real places. Agents are given driving instructions which they must learn to interpret in order to successfully navigate in this environment. Since humans equipped with driving instructions can readily navigate in previously unseen cities, we set a high bar and test our trained agents for similar cognitive capabilities. Although deep reinforcement learning (RL) methods are frequently evaluated only on data that closely follow the training distribution, our dataset extends to multiple cities and has a clean train/test separation. This allows for thorough testing of generalisation ability. This paper presents the StreetNav environment and tasks, models that establish strong baselines, and extensive analysis of the task and the trained agents.
Quantifying contribution and propagation of error from computational steps, algorithms and hyperparameter choices in image classification pipelines
Venue: arXiv cs.CV 2019
Year: 2019

Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for performing a particular function. Each algorithm consists of several hyperparameters. Algorithms and hyperparameters must be optimized as a whole to produce the best performance. Typical machine learning pipelines consist of complex algorithms in each of the steps. Not only is the selection process combinatorial, but it is also important to interpret and understand the pipelines. We propose a method to quantify the importance of different components in the pipeline, by computing an error contribution relative to an agnostic choice of computational steps, algorithms and hyperparameters. We also propose a methodology to quantify the propagation of error from individual components of the pipeline with the help of a naive set of benchmark algorithms not involved in the pipeline. We demonstrate our methodology on image classification pipelines. The agnostic and naive methodologies quantify the error contribution and propagation respectively from the computational steps, algorithms and hyperparameters in the image classification pipeline. We show that algorithm selection and hyperparameter optimization methods like grid search, random search and Bayesian optimization can be used to quantify the error contribution and propagation, and that random search is able to quantify them more accurately than Bayesian optimization. This methodology can be used by domain experts to understand machine learning and data analysis pipelines in terms of their individual components, which can help in prioritizing different components of the pipeline.
Improving fraud prediction with incremental data balancing technique for massive data streams
Venue: arXiv cs.LG 2019
Year: 2019

The performance of classification algorithms with a massive and highly imbalanced data stream depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied in the past with Batch data to resolve the class imbalance problem. This paper proposes a new incremental data balancing framework which can work with massive imbalanced data streams. In this paper, we choose Racing Algorithm as an automated data balancing technique which optimizes the balancing techniques. We applied Random Forest classification algorithm which can deal with the massive data stream. We investigated the suitability of Racing Algorithm and Random Forest in the proposed framework. Applying new technique in the proposed framework on the European Credit Card dataset, provided better results than the Batch mode. The proposed framework is more scalable to handle online massive data streams.
SPOILER: Speculative Load Hazards Boost Rowhammer and Cache Attacks
Venue: arXiv cs.CR 2019
Year: 2019

Modern microarchitectures incorporate optimization techniques such as speculative loads and store forwarding to improve the memory bottleneck. The processor executes the load speculatively before the stores, and forwards the data of a preceding store to the load if there is a potential dependency. This enhances performance since the load does not have to wait for preceding stores to complete. However, the dependency prediction relies on partial address information, which may lead to false dependencies and stall hazards. In this work, we are the first to show that the dependency resolution logic that serves the speculative load can be exploited to gain information about the physical page mappings. Microarchitectural side-channel attacks such as Rowhammer and cache attacks like Prime+Probe rely on the reverse engineering of the virtual-to-physical address mapping. We propose the SPOILER attack which exploits this leakage to speed up this reverse engineering by a factor of 256. Then, we show how this can improve the Prime+Probe attack by a 4096 factor speed up of the eviction set search, even from sandboxed environments like JavaScript. Finally, we improve the Rowhammer attack by showing how SPOILER helps to conduct DRAM row conflicts deterministically with up to 100% chance, and by demonstrating a double-sided Rowhammer attack with normal user's privilege. The later is due to the possibility of detecting contiguous memory pages using the SPOILER leakage.
On the boomerang uniformity of (quadratic) permutations over $F_{2^n}$
Venue: arXiv cs.CR 2019
Year: 2019

At Eurocrypt'18, Cid, Huang, Peyrin, Sasaki, and Song introduced a new tool called Boomerang Connectivity Table (BCT) for measuring the resistance of a block cipher against the boomerang attack (which is an important cryptanalysis technique introduced by Wagner in 1999 against block ciphers). Next, Boura and Canteaut introduced an important parameter (related to the BCT) for cryptographic Sboxes called boomerang uniformity. In this context, we present a brief state-of-the-art on the notion of boomerang uniformity of vectorial functions (or Sboxes) and provide new results. More specifically, we present a slightly different (and more convenient) formulation of the boomerang uniformity and show that the row sum and the column sum of the boomerang connectivity table can be expressed in terms of the zeros of the second-order derivative of the permutation or its inverse. Most importantly, we specialize our study of boomerang uniformity to quadratic permutations in even dimension and generalize the previous results on quadratic permutation with optimal BCT (optimal means that the maximal value in the Boomerang Connectivity Table equals the lowest known differential uniformity). As a consequence of our general result, we prove that the boomerang uniformity of the binomial differentially $4$-uniform permutations presented by Bracken, Tan, and Tan equals $4$. This result gives rise to a new family of optimal Sboxes.
Automatic Techniques to Systematically Discover New Heap Exploitation Primitives
Venue: arXiv cs.CR 2019
Year: 2019

Heap exploitation techniques to abuse the metadata of allocators have been widely studied since they are application independent and can be used in restricted environments that corrupt only metadata. Although prior work has found several interesting exploitation techniques, they are ad-hoc and manual, which cannot effectively handle changes or a variety of allocators. In this paper, we present a new naming scheme for heap exploitation techniques that systematically organizes them to discover the unexplored space in finding the techniques and ArcHeap, the tool that finds heap exploitation techniques automatically and systematically regardless of their underlying implementations. For that, ArcHeap generates a set of heap actions (e.g. allocation or deallocation) by leveraging fuzzing, which exploits common designs of modern heap allocators. Then, ArcHeap checks whether the actions result in impact of exploitations such as arbitrary write or overlapped chunks that efficiently determine if the actions can be converted into the exploitation technique. Finally, from these actions, ArcHeap generates Proof-of-Concept code automatically for an exploitation technique. We evaluated ArcHeap with real-world allocators --- ptmalloc, jemalloc, and tcmalloc --- and custom allocators from the DARPA Cyber Grand Challenge. ArcHeap successfully found 14 out of 16 known exploitation techniques and found five new exploitation techniques in ptmalloc. Moreover, ArcHeap found several exploitation techniques for jemalloc, tcmalloc, and even for the custom allocators. Further, ArcHeap can automatically show changes in exploitation techniques along with version change in ptmalloc using differential testing.
Improved Differentially Private Analysis of Variance
Venue: arXiv cs.CR 2019
Year: 2019

Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines. Analysis of variance (ANOVA) in particular is used to detect dependence between a categorical and a numerical variable. Here we show how one can carry out this hypothesis test under the restrictions of differential privacy. We show that the $F$-statistic, the optimal test statistic in the public setting, is no longer optimal in the private setting, and we develop a new test statistic $F_1$ with much higher statistical power. We show how to rigorously compute a reference distribution for the $F_1$ statistic and give an algorithm that outputs accurate $p$-values. We implement our test and experimentally optimize several parameters. We then compare our test to the only previous work on private ANOVA testing, using the same effect size as that work. We see an order of magnitude improvement, with our test requiring only 7% as much data to detect the effect.
JIM: Joint Influence Modeling for Collective Search Behavior
Venue: arXiv cs.LG 2019
Year: 2019

Previous work has shown that popular trending events are important external factors which pose significant influence on user search behavior and also provided a way to computationally model this influence. However, their problem formulation was based on the strong assumption that each event poses its influence independently. This assumption is unrealistic as there are many correlated events in the real world which influence each other and thus, would pose a joint influence on the user search behavior rather than posing influence independently. In this paper, we study this novel problem of Modeling the Joint Influences posed by multiple correlated events on user search behavior. We propose a Joint Influence Model based on the Multivariate Hawkes Process which captures the inter-dependency among multiple events in terms of their influence upon user search behavior. We evaluate the proposed Joint Influence Model using two months query-log data from https://search.yahoo.com/. Experimental results show that the model can indeed capture the temporal dynamics of the joint influence over time and also achieves superior performance over different baseline methods when applied to solve various interesting prediction problems as well as real-word application scenarios, e.g., query auto-completion.
Equilibrated Recurrent Neural Network: Neuronal Time-Delayed Self-Feedback Improves Accuracy and Stability
Venue: arXiv cs.LG 2019
Year: 2019

We propose a novel {\it Equilibrated Recurrent Neural Network} (ERNN) to combat the issues of inaccuracy and instability in conventional RNNs. Drawing upon the concept of autapse in neuroscience, we propose augmenting an RNN with a time-delayed self-feedback loop. Our sole purpose is to modify the dynamics of each internal RNN state and, at any time, enforce it to evolve close to the equilibrium point associated with the input signal at that time. We show that such self-feedback helps stabilize the hidden state transitions leading to fast convergence during training while efficiently learning discriminative latent features that result in state-of-the-art results on several benchmark datasets at test-time. We propose a novel inexact Newton method to solve fixed-point conditions given model parameters for generating the latent features at each hidden state. We prove that our inexact Newton method converges locally with linear rate (under mild conditions). We leverage this result for efficient training of ERNNs based on backpropagation.
Extreme Channel Prior Embedded Network for Dynamic Scene Deblurring
Venue: arXiv cs.CV 2019
Year: 2019

Recent years have witnessed the significant progress on convolutional neural networks (CNNs) in dynamic scene deblurring. While CNN models are generally learned by the reconstruction loss defined on training data, incorporating suitable image priors as well as regularization terms into the network architecture could boost the deblurring performance. In this work, we propose an Extreme Channel Prior embedded Network (ECPeNet) to plug the extreme channel priors (i.e., priors on dark and bright channels) into a network architecture for effective dynamic scene deblurring. A novel trainable extreme channel prior embedded layer (ECPeL) is developed to aggregate both extreme channel and blurry image representations, and sparse regularization is introduced to regularize the ECPeNet model learning. Furthermore, we present an effective multi-scale network architecture that works in both coarse-to-fine and fine-to-coarse manners for better exploiting information flow across scales. Experimental results on GoPro and Kohler datasets show that our proposed ECPeNet performs favorably against state-of-the-art deep image deblurring methods in terms of both quantitative metrics and visual quality.
Reliable Access to Massive Restricted Texts: Experience-based Evaluation
Venue: arXiv cs.PF 2019
Year: 2019

Libraries are seeing growing numbers of digitized textual corpora that frequently come with restrictions on their content. Computational analysis corpora that are large, while of interest to scholars, can be cumbersome because of the combination of size, granularity of access, and access restrictions. Efficient management of such a collection for general access especially under failures depends on the primary storage system. In this paper, we identify the requirements of managing for computational analysis a massive text corpus and use it as basis to evaluate candidate storage solutions. The study based on the 5.9 billion page collection of the HathiTrust digital library. Our findings led to the choice of Cassandra 3.x for the primary back end store, which is currently in deployment in the HathiTrust Research Center.
Revenue Maximization with Imprecise Distribution
Venue: arXiv cs.GT 2019
Year: 2019

We study the revenue maximization problem with an imprecisely estimated distribution of a single buyer or several independent and identically distributed buyers given that this estimation is not far away from the true distribution. We use the earth mover's distance to capture the estimation error between those two distributions in terms of both values and their probabilities, i.e., the error in value space given a quantile, and the error in quantile space given a value. We give explicit characterization of the optimal mechanisms for the single buyer setting. For the multi-buyer case, we provide an algorithm that finds an approximately optimal mechanism (FPTAS) among the family of second price auctions with a fixed reserve.
CodeTrolley: Hardware-Assisted Control Flow Obfuscation
Venue: arXiv cs.CR 2019
Year: 2019

Many cybersecurity attacks rely on analyzing a binary executable to find exploitable sections of code. Code obfuscation is used to prevent attackers from reverse engineering these executables. In this work, we focus on control flow obfuscation - a technique that prevents attackers from statically determining which code segments are original, and which segments are added in to confuse attackers. We propose a RISC-V-based hardware-assisted deobfuscation technique that deobfuscates code at runtime based on a secret safely stored in hardware, along with an LLVM compiler extension for obfuscating binaries. Unlike conventional tools, our work does not rely on compiling hard-to-reverse-engineer code, but on securing a secret key. As such, it can be seen as a lightweight alternative to on-the-fly binary decryption.
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network
Venue: arXiv cs.CV 2019
Year: 2019

Crowd counting has recently attracted increasing interest in computer vision but remains a challenging problem. In this paper, we propose a trellis encoder-decoder network (TEDnet) for crowd counting, which focuses on generating high-quality density estimation maps. The major contributions are four-fold. First, we develop a new trellis architecture that incorporates multiple decoding paths to hierarchically aggregate features at different encoding stages, which can handle large variations of objects. Second, we design dense skip connections interleaved across paths to facilitate sufficient multi-scale feature fusions and to absorb the supervision information. Third, we propose a new combinatorial loss to enforce local coherence and spatial correlation in density maps. By distributedly imposing this combinatorial loss on intermediate outputs, gradient vanishing can be largely alleviated for better back-propagation and faster convergence. Finally, our TEDnet achieves new state-of-the art performance on four benchmarks, with an improvement up to 14% in terms of MAE.